To alleviate privacy concerns, some recent works (e.g., [1]}, [2]}, [3]}) adopted federated learning to jointly train models on the edge clients without centralizing data.
The federated learning-based approaches enable the sensitive data to be utilized in situ (e.g., [2]}, [3]}),
while different edge clients can collaboratively update models by aggregating the gradients or parameters (e.g., [1]}) without sharing private data.
